Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2021 | OriginalPaper | Buchkapitel

Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark

verfasst von : Reem Abdel-Salam, Rana Mostafa, Mayada Hadhood

Erschienen in: Deep Learning for Human Activity Recognition

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Fußnoten
1
Recent works are implemented using the same architecture and hyper-parameters as mentioned in their papers and re-evaluated using proposed standardized benchmark.
 
Literatur
1.
Abidine, B.M., Fergani, L., Fergani, B., Oussalah, M.: The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition. Pattern Anal. Appl. 21, 119–138 (2016)MathSciNetCrossRef
2.
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: ESANN (2013)
3.
4.
5.
Burns, D.M., Whyne, C.M.: Personalized activity recognition with deep triplet embeddings. arXiv abs/2001.05517 (2020)
6.
Catal, C., Tufekci, S., Pirmit, E., Kocabag, G.: On the use of ensemble of classifiers for accelerometer-based activity recognition. Appl. Soft Comput. 37, 1018–1022 (2015)CrossRef
7.
Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 168–172 (2015)
8.
9.
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2247–2253 (2007)CrossRef
10.
Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381–388 (2016)
11.
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2065), 2015020 (2016)
12.
Jordao, A., Nazare, A.C., Sena, J.S.: Human activity recognition based on wearable sensor data: a standardization of the state-of-the-art (2018)
13.
14.
Kasteren, T.V., Englebienne, G., Kröse, B.: Human activity recognition from wireless sensor network data: benchmark and software (2011)
15.
Kwapisz, J.R., Weiss, G.M., Moore, S.: Activity recognition using cell phone accelerometers. SIGKDD Explor. 12, 74–82 (2011)CrossRef
16.
Lyu, L., He, X., Law, Y.W., Palaniswami, M.: Privacy-preserving collaborative deep learning with application to human activity recognition. In: CIKM 2017 (2017)
17.
Panwar, M., et al.: CNN based approach for activity recognition using a wrist-worn accelerometer. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2438–2441 (2017)
18.
Saeed, A., Ozcelebi, T., Lukkien, J.: Multi-task self-supervised learning for human activity detection. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 3, 1–30 (2019)CrossRef
19.
Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36 (2004)
20.
Sun, J., Fu, Y., Li, S., He, J., Xu, C., Tan, L.: Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors. J. Sens. 2018, 8580959:1–8580959:10 (2018)
21.
22.
Weiss, G., Lockhart, J.W., Pulickal, T., McHugh, P.T., Ronan, I.H., Timko, J.L.: Actitracker: a smartphone-based activity recognition system for improving health and well-being. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 682–688 (2016)
23.
Xia, K., Huang, J., Wang, H.: LSTM-CNN architecture for human activity recognition. IEEE Access 8, 56855–56866 (2020)CrossRef
24.
Zhang, M., Sawchuk, A.A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: UbiComp 2012 (2012)
Metadaten
Titel
Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark
verfasst von
Reem Abdel-Salam
Rana Mostafa
Mayada Hadhood
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Deep Learning for Human Activity Recognition

Print ISBN: 978-981-16-0574-1

Electronic ISBN: 978-981-16-0575-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-16-0575-8_1

Premium Partner

    Bildnachweise